Automate a lot of works
Accurate
The global AI market is expected to grow from 40 billion in 2020 to 51 billion in 2021 at a compound annual growth rate (CAGR) of 28.4%. The market is expected to reach 171 billion in 2025 at a CAGR of 35%.
An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Data :
Hypothesis function:
hθ(x) = 4x at θ = 4
hθ(x) = 4x at θ = 4
x = [1,2,3]
Cost function : J(θ)=(1/2m)*sum[(Y - h)^2]
J(4) =(1/6)*sum((2-4)^2 + (4-8)^2 + (6-12)^2) = 56/6 = 9.333
Optimum θ is 2 : Best fitted model is H = 2X.
At θ:= θ – alpha* d/dθJ(θ) at θ =4 assuming d/dθJ(4) = 2.
If the learning rate (alpha) = 0.1,
θ:= 4 - 0.1 2 = 3.8
If the learning rate (alpha) = 0.5
θ:= 4 – 0.5 2 = 3
Initial Hypothesis function - hθ(x) = 4x
Cost function - J(θ) = (1/2m)sum[(Y - h)^2] = 1/(23)* sum(((2-4)^2 + (4-8)^2 + (6-12))^2)) = 56/6=9.333
Gradient Descent – θ := θ – alpha d/dθJ(4) = 4 – 0.12 = 3.8
Modified Hypothesis function - hθ(x) = 3.8x
At second iteration,
Initial Hypothesis function - hθ(x) = 3.8x
Cost function - J(θ) = 1/(23) sum(((2-3.8)^2 + (4-7.6)^2 + (6-11.4)^2)) = 45.36/6 = 7.56
Gradient Descent – θ := θ – alpha d/dθ J(3.8) = 3.8 – 0.12 = 3.6
Final Hypothesis function - hθ(x) = 3.6x
And the iteration will go on until the cost function gest to 0 like J(θ)=0 or it no longer decreases or no more data.

The cost function decreases and slope (θ) decreases as well to the optimum point.

Interviewer: What’s your biggest strength ?
Me: I’m an expert in Machine Learning.
Interviewer: What’s 9 + 10 ?
Me: It is 3.
Interviewer: Not even close. It’s 19.
Me: It’s 16.
Interviewer: Wrong. It's still 19.
Me: It’s 18.
Interviewer: No, it’s 19… Arghhh
Me: It’s 19.
Interviewer: You are hired.